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existZalo has become a must-have platform for marketing in Vietnam today.How to extract batchesZalo user's gender and age tags, has become one of the most concerned operations for many private domain traders, e-commerce pitchers and brands. After all, only the target user is clearly knownOnly when you decide "who is it?" can you decide "how to say it".
But the problem is——Zalo does not publicly display age and gender information like Facebook or Instagram, so how can we quickly judge and extract it accurately? Are there any tools that are efficient, accurate and able to support batches?
This article will answer:It can be used on the market nowSeveral mainstream methods and tools for extracting the age tags of Zalo, and recommended practical application solutions.
Why do you need it"Batch Extraction" Zalo Gender Age Tag?
The number of Zalo accounts is often thousands, and manually viewing avatars, nicknames, and interactive content is not only time-consuming and labor-intensive, but also has extremely low accuracy. Only by using the tools to achieve "batch identification + label marking" can you:
lQuickly filter target user groups (e.g.Women aged 20-35)
lPrecise marketing content (content varies greatly in different age groups)
lReduce ineffective communication costs (avoid waste on non-target users)
lIntegrate into a complete user portrait (dockingCRM or marketing system)
Tool 1:AI Identification Platform (Recommended)
The most mature one is nowBatch extractionThe most effective way to use the Zalo user’s gender age tag, is used specificallyAI data recognition platform, such as the Zalo detection and filtering function provided by [Digital Planet].
The platform has the following advantages:
lHigh accuracy of gender recognition: Comprehensive identification based on avatar, nickname, interactive behavior and other models
lAge classification is clear: Provide age tags, such as18-25, 25-35, 35-45, etc.
lSupport batch import of mobile phone numbers: Tens of thousands of tests at one timeZalo account
lSupport tag filtering and export: Extract only"Female + 25-35 years old + active" data
lNo need to log in to the account, no risk operation: Offline inspection throughout the process, safe and reliable
You only need to upload your mobile phone number file and you can get complete labeled data within a few minutes, which is directly used for delivery, mass sending, private messages and other actions.
Suitable for:
Small and medium-sized brand overseas team, data studio,Zalo private domain pitchers, e-commerce SaaS companies, AI marketing agencies, etc.
Tool 2:Zalo official function + manual discrimination (low efficiency)
Zalo does not directly provide gender or age fields in its official app or web version. However, some users will set more obvious avatars and nicknames, or reveal their birthday information in their personal profile.
At this time, you can:
lpassManually view avatar and nicknamePreliminary gender judgment
lEstimate age range based on avatar age characteristics
lInteract with the account to see its tone and content style
lEstimation of behavioral characteristics such as posting time and social frequency
However, these methods are only suitable for small batch operations and cannot meet the needs of systematic screening. Moreover, accuracy is highly dependent on manual experience and is prone to errors.
Suitable for:
It is not suitable for official placement during the testing phase or early operation.
Tool 3: Crawler or plug-in script (high risk)
There are some claims on the Internet that canThe script tool for "extracting Zalo user information" uses simulated login to obtain public information of Zalo users. However:
lMost need to log inZalo account operation, easy to trigger risk control
lSome scripts have not been updated and the interface has expired
lCan't judge gender and age, only avatars andID
lHigh legal risks and prone to violationsZalo Terms of Service
Using this type of tool is time-consuming, risky and high, and the data structure is incomplete, so it is not recommended for enterprises or formal business teams to use it.
Suitable for:
Internal testing purposes of technical teams, non-mainstream commercial solutions.
Common misunderstandings about the extraction of Zalo gender age tags
In actual operation, many people extractThere are some misunderstandings about Zalo’s gender and age label:
1.ThinkZalo has no way to judge age and gender
In fact, it is OK. AI models can comprehensively infer age and gender through avatars, nicknames, and behavioral characteristics.
2.I thought I could only read the information by logging in to the account
Platforms such as Digital Planet provide mobile phone number detection without logging into a Zalo account to ensure security and privacy compliance.
3.Is it reliable to judge gender and age only by looking at the avatar?
Indeed, single point of information cannot be judged, but AI can integrate dozens of dimensions to comprehensively identify it to improve accuracy.
4.Worry about slow batch extraction?
Professional platforms can usually complete the analysis and marking of tens of thousands of accounts in a few minutes, which is much more efficient than manual.
A practical scenario: education and training industryZalo launches
A company that does online Chinese education is preparing to expand parent users in Vietnam. The team purchased it150,000 Vietnamese mobile phone numbers are ready to establish a private domain pool.
But what bothers them is:
lAre these mobile phone numbers registeredZalo?
lAn active user?
lIs it trueWomen aged 30-45 (parent group)?
Using Digital PlanetAfter Zalo filters the platform, they completed the following operations:
lExclude unregisteredZalo's number
lRemove no avatar or zombie account
lKeep only"Female + Active + 30-45 years old" users, a total of about 28,000
lFurther judge consumption capacity based on the device model, and give priority to mid-to-high-end device users
This accurate pool is directly imported into the community and private message system, and the conversion rate of the first round of activities has increased2.5 times, the feedback effect is far beyond expectations.
How to quickly obtain tag data using the Digital Planet Platform?
1.Get readyZalo target mobile number list
2.Register and log in to the [Digital Planet] platform
3.Upload your mobile phone number and detect the activation status and gender age tags with one click
4.Export and use data for filtering required conditions (supportedExcel format)
5.Apply to private message marketing, mass sending, community recruitment,CRM completion scenarios
No need for cumbersome settings, no need to log inZalo account, the entire process is controllable and traceable.
Digital PlanetIt is a world-leading number screening platform that combinesGlobal mobile phone number segment selection, number generation, deduplication, comparison and other functions. It supports global customersBulk numbers from 236 countriesFiltering and testing services, currently supportedMore than 40 social and apps, such as:
The platform has several features, includingOpen filtering, active filtering, interactive filtering, gender filtering, avatar filtering, age filtering, online filtering, accurate filtering, duration filtering, power-on filtering, empty number filtering, mobile device filteringwait.
Platform providesSelf-sieve mode, sieve mode, fine sieve mode and custom mode, to meet the needs of different users.
Its advantage lies in the integration of major social and applications around the world, providing one-stop, real-time and efficient number screening services to help you achieve global digital development.
You can use the official channelt.me/xingqiuproGet more information and verify the identity of business personnel through the official website. Official Businesstelegram:@xq966
(Warm reminder: You must identify the username when searching for the official customer service number on Telegramxq966), you can also verify through the official website:https://www.xingqiu.pro/check.html, confirm whether the business you are in contact with is a planet official
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